class_docstr: Wrapper around MelSpectrogram torchaudio transform providing proper padding
  and additional post-processing including log scaling.
  
  Args:
      n_mels (int): Number of mel bins.
      n_fft (int): Number of fft.
      hop_length (int): Hop size.
      win_length (int): Window length.
      n_mels (int): Number of mel bins.
      sample_rate (int): Sample rate.
      f_min (float or None): Minimum frequency.
      f_max (float or None): Maximum frequency.
      log (bool): Whether to scale with log.
      normalized (bool): Whether to normalize the melspectrogram.
      floor_level (float): Floor level based on human perception (default=1e-5).
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code: def forward(self, x):
      p = int((self.n_fft - self.hop_length) // 2)
      if len(x.shape) == 2:
          x = x.unsqueeze(1)
      x = F.pad(x, (p, p), "reflect")
      # Make sure that all the frames are full.
      # The combination of `pad_for_conv1d` and the above padding
      # will make the output of size ceil(T / hop).
      x = pad_for_conv1d(x, self.n_fft, self.hop_length)
      self.mel_transform.to(x.device)
      mel_spec = self.mel_transform(x)
      B, C, freqs, frame = mel_spec.shape
      if self.log:
          mel_spec = torch.log10(self.floor_level + mel_spec)
      return mel_spec.reshape(B, C * freqs, frame)
  
file_path: audiocraft/audiocraft/losses/specloss.py
imports: import typing
  import numpy
  from torchaudio.transforms import MelSpectrogram
  import torch
  from torch import nn
  from torch.nn import functional
  from ..modules import pad_for_conv1d
label_desc: Use image node_id 2 for calling the MelSpectrogramWrapper obj's underlying member method code with example usage: obj.forward(x) and returns: mel_spec